In previous work [1]}, [2]}, we presented a PrPM technique that considers the tradeoff between triggering an intervention now versus later when resources are limited. This technique relies on estimates including the intervention effect (or conditional average treatment effect, i.e., \(CATE\) ), total uncertainty (determined as the entropy of the average prediction from an ensemble of machine learning (ML) classifiers), and the \(\mathbb {P}(uout)\) . However, these uncertainty estimates do not come with confidence guarantees. This latter approach is used as a baseline in the empirical evaluation reported later in this paper.
